<p dir="ltr">A building layout consists of a set of buildings in city blocks defined by a network of roads. Modeling and generating large-scale urban building layouts is of significant interest in computer vision, computer graphics, and urban applications. Researchers seek to obtain building features (e.g. building shapes, counts, and areas) at large scales. However, data quality and data equality challenge the generation and extraction of building features. Blurriness, occlusions, and noise from prevailing satellite images severely hinders performance of image segmentation, super-resolution, or deep-learning based translation networks. Moreover, large-scale urban layout generation struggles with complex and arbitrary shapes of building layouts, and context-sensitive nature of the city morphology, which prior approaches have not considered. Facing the challenges of data quality, generation robustness, and context-sensitivity of urban layout generation, In this thesis, we first address the data quality problem by combing globally-available satellite images and spatial geometric feature datasets, in order to create a generative modeling framework that enables obtaining significantly improved accuracy in per-building feature estimation as well as generation of visually plausible building footprints. Secondly, for generation robustness, We observe that building layouts are discrete structures, consisting of multiple rows of buildings of various shapes, and are amenable to skeletonization for mapping arbitrary city block shapes to a canonical form. In that, we propose a fully automatic approach to building layout generation using graph attention networks. The method generates realistic urban layouts given arbitrary road networks, and enables conditional generation based on learned priors. Nevertheless, we propose the approach addresses context-sensitivity by leveraging a canonical graph representation for the entire city, which facilitates scalability and captures the multi-layer semantics inherent in urban layouts. We introduce a novel graph-based masked autoencoder (GMAE) for city-scale urban layout generation. The method encodes attributed buildings, city blocks, communities and cities into a unified graph structure, enabling self-supervised masked training for graph autoencoder. Additionally, we employ scheduled iterative sampling for 2.5D layout generation, prioritizing the generation of important city blocks and buildings. Our method has proven its robustness by large-scale prototypical experiments covering heterogeneous scenarios from dense urban to sparse rural. It achieves good realism, semantic consistency, and correctness across the heterogeneous urban styles in 330 US cities. </p>

This diploma thesis deals with image super-resolution with conservation of good quality. Firstly, there are described state of the art methods dealing with this problem, as well as principles of neural networks with focus on convolutional ones. Finally, there is described a few models of convolutional neural network for image super-resolution to double size, which have been trained, tested and compared on newly created database with pictures of people.

Recently, a variety of machine learning techniques have been applied to different healthcare sectors, and the results appear to be promising. One such sector is healthcare planning, in which patient data is used to produce statistical models for predicting the load on different units of the healthcare system. This research introduces an attempt to design and implement a privacy-preserving synthetic data generation method adapted explicitly to patients’ health data and for healthcare planning. A Privacy-preserving Conditional Generative Adversarial Network (PPCGAN) is used to generate synthetic data of Healthcare events, where a well-designed noise is added to the gradients in the training process. The concept of differential privacy is used to ensure that adversaries cannot reveal the exact training samples from the trained model. Notably, the goal is to produce digital patients and model their journey through the healthcare system. / Nyligen har en mängd olika maskininlärningstekniker tillämpats på olika hälso- och sjukvårdssektorer, och resultaten verkar lovande. En sådan sektor är vårdplanering, där patientdata används för att ta fram statistiska modeller för att förutsäga belastningen på olika enheter i sjukvården. Denna forskning introducerar ett försök att utforma och implementera en sekretessbevarande syntetisk datagenereringsmetod som uttryckligen anpassas till patienters hälsodata och för vårdplanering. Ett sekretessbevarande villkorligt generativt kontradiktoriskt nätverk (PPCGAN) används för att generera syntetisk data från hälsovårdshändelser, där ett väl utformat brus läggs till gradienterna i träningsprocessen. Begreppet differentiell integritet används för att säkerställa att motståndare inte kan avslöja de exakta träningsproven från den tränade modellen. Målet är särskilt att producera digitala patienter och modellera deras resa genom sjukvården.